Discrimination of unsound soybeans using hyperspectral imaging: A deep learning method based on dual-channel feature...

The application of high-level data fusion in the detection of agricultural products still presents a significant challenge. In this study, dual-channel feature fusion model (DCFFM) with attention mechanism was proposed to optimize the utilization of both one-dimensional spectral data and two-dimensional image data in the hyperspectral images for achieving high-level data fusion. A comparative analysis of support vector machine (SVM), convolutional neural network (CNN) with DCFFM, demonstrated that DCFFM exhibited superior results, achieving the accuracy, precision, recall, specificity, and F1-score of 95.13 %, 95.49 %, 94.83 %, 98.97 %, 95.12 % in the visible and near-infrared (Vis-NIR), and 94.00 %, 94.43 %, 94.16 %, 98.67 %, 94.27 % in the short-wave infrared (SWIR). This also indicated that Vis-NIR was more suitable for identifying unsound soybeans than SWIR. Furthermore, visualization was employed to demonstrate classification outcomes, thereby illustrating the generalization capacity of DCFFM through model inversion. In summary, this study is to explore a modeling framework that is capable of the comprehensive acquisition of spectra and images in the hyperspectral images, allowing for high-level data fusion, thereby achieving enhanced levels of accuracy.